Detection of Emotions during Learning with AutoTutor

By P. Chipman, S. D’Mello, Barry Gholson, A. Graesser, Bethany McDaniel, Amy M. Witherspoon. 2006

150 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dc675fcf84701e1270cd4b5d2233fb1a855e5c71

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Citations in the corpus

Automatic detection of learner’s affect from conversational cues (2008, background): “Our previous research has identified the affective states that accompany learning introductory computer literacy (Craig et al., 2004; D'Mello et al., 2006) and some alternative ways to measure these affective states (Graesser et al., 2006).”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, methodology): “The data used to train the affect detectors were obtained from an existing study (Graesser et al. 2006) involving 28 learners being tutored by AutoTutor, an intelligent tutoring system with conversational dialogue, as will be described below.”
Dynamics of affective states during complex learning (2012, methodology): “Both studies involved a 30e35 min learning session after which participants provided self-reported affect judgments at approximately 100 points in their tutoring sessions via a retrospective affect judgment protocol (Graesser et al., 2006).”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, methodology): “Judgments were based on videos of students’ faces and computer screens which were recorded during the tutorial session [Graesser et al. 2006].”
OpenFace: An open source facial behavior analysis toolkit (2016, background): “Other uses of automatic facial behavior analysis include automotive industries [14], education [42, 26], and entertainment [47].”